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How Does Visual Impairment Affect Performance on Tasks of Everyday Life?
The SEE Project
Arch Ophthalmol. 2002;120:774-780.
ABSTRACT
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Objective To determine the association between performance on selected tasks of
everyday life and impairment in visual acuity and contrast sensitivity.
Methods Visual acuity and contrast sensitivity were obtained on a population-based
sample of 2520 older African American and white subjects. Performance was
assessed on mobility, daily activities with a strong visual component, and
visually intensive tasks. Disability was defined as performance less than
1 SD below the mean. Receiver operating characteristic curve analyses were
used to evaluate the sensitivity and specificity of thresholds in acuity and
contrast loss for determining disability.
Results Both visual acuity and contrast sensitivity loss were associated with
decrements in function. The relationship of function to the vision measures
was mostly linear, therefore, receiver operating characteristic curves were
not helpful in identifying cutoff points for predicting disabilities. For
mobility tasks, most persons were not disabled until they had significant
acuity loss (logMAR visual acuity >1.0 or <20/200) or contrast sensitivity
loss (0.9 log units contrast sensitivity). For heavily visually intensive
tasks, like reading, visual acuity worse than 0.2 logMAR (20/30) or contrast
sensitivity worse than 1.4 log units was disabling.
Conclusions Both contrast sensitivity and visual acuity loss contribute independently
to deficits in performance on everyday tasks. Defining disability as deficits
in performance relative to a population, it is possible to identify visual
acuity and contrast loss where most are disabled. However, the cutoff points
depend on the task, suggesting that defining disability using a single threshold
for visual acuity or contrast sensitivity loss is arbitrary.
INTRODUCTION
THE LINK BETWEEN visual impairment and physical disability, psychosocial
distress, and general quality of life has been a growing area of research,
and a number of studies including our own have documented the decrements in
function associated with loss of various components of visual function.1-9
In all but a few of these, the measure of visual impairment was confined to
either self-report of visual loss, or a measure of visual acuity.
Yet, there are data that suggest other measures of visual function are
also important for predicting functional loss. Rubin et al3
have shown that a 2-fold reduction in contrast sensitivity is associated with
3- to 5-fold odds of self-report of difficulty on everyday tasks, and the
effect is independent of the contribution of visual acuity loss. Others have
linked loss of contrast sensitivity to mobility and balance decrements.10-11
In the Salisbury Eye Evaluation (SEE) Project, our goal is to determine,
in a population-based sample of older adults, the relationship of visual impairment
with functional disability. Measures of functional disability include both
self-report of difficulty and actual performance on a variety of tasks.12 The tasks chosen are meaningful for comparisons with
tasks of everyday life, as we have previously shown that performance on these
tasks mirrors the performance on the same tasks conducted in the home.13 In this article, we show the relationship of decrements
in visual acuity and contrast sensitivity to decrements in the performance
on a variety of tasks, and try to determine meaningful cutoff points in loss
of visual acuity or contrast that are rooted in functional loss.
PARTICIPANTS AND METHODS
POPULATION
The SEE Project is a population-based, longitudinal study of the effect
of visual impairment, and age-related eye diseases, on functional status in
older, community-dwelling adults.12 To achieve
the aims of this project, a random sample of residents of Salisbury, Md, aged
from 65 to 84 years, was recruited for a home interview and an examination
at the SEE clinic. The sample was selected from the Health Care Financing
Administration Medicare database. Eligibility criteria excluded those who
were institutionalized or completely housebound, and those who scored less
than 18 on the Mini-Mental State Examination (MMSE).14
Written, informed consent was obtained at the home interview in accord with
the tenets of the Declaration of Helsinki. Details on the population and recruitment
are described elsewhere.15 In summary, of the
original sample, 73% participated in the home interview and 65% participated
in both the interview and the clinical examination.
Permission was also sought to administer a 12-question screener questionnaire
to both the refusals and the participants, to investigate the comparability
between those for whom data were available and those who refused. Of the 1301
refusals to the clinic examination or the home questionnaire, 65% agreed to
answer the questions on the screener. There were no differences by age, race,
or sex between the refusals who answered the screener and those who did not
answer the screener.15 There was no difference
in participation rates by race; participation rates declined with age from
68% in the age group 65 through 69 years to 55% in age group 80 through 84
years. There was no difference in the sex- and age-adjusted proportion rating
their vision as 6 or better, on a scale of 1 to10 where 10 was excellent.
Among participants, 82.9% were 6 or better compared with 84.0% among refusals.
These data provide some assurances that the sample of 2520 participants did
not seem to be biased on self-reported vision status.
MEASURES OF HEALTH
The General Health Questionnaire was administered in the clinic; the
subscale on depressive symptoms was used as a measure of depression. Cognitive
status was assessed at home visit using the MMSE; possible scores for this
study range from 18 to 30.
Data on diabetes mellitus were based on self-report, validated by use
of insulin or oral hypoglycemics, or by hospital or physician records. For
those who did not report having diabetes mellitus, we included as having diabetes
mellitus those with a hemoglobin A1c value greater than 7%.16 Hypertension was based on self-report plus evidence
of taking an antihypertension medication. In addition, blood pressure was
measured in a sitting position, 3 times, according to a standard protocol.17 Persons who did not report hypertension but had an
average diastolic pressure of 90 mm Hg or higher or a systolic pressure of
160 mm Hg or higher were classified as subjects possibly having hypertension
for our study. Other conditions were based on self-report such as stroke,
cancer, parkinsonism, arthritis, myocardial infarction, congestive heart failure,
and pulmonary problems. We calculated an index of number of comorbid conditions
(excluding cataract).
MEASURES OF VISION
Details on vision testing have been described previously.18
All tests were administered by a trained technician using forced-choice procedures.
In essence, distance acuity was measured using Early Treatment Diabetic Retinopathy
Study charts, monocularly and binocularly, with habitual correction followed
by best correction after subjective refraction. Visual acuity was scored as
the number of letters read correctly, and converted to logMAR scores (logMAR
is the logarithm of the minimum angle of resolution). Contrast sensitivity
was measured with the Pelli-Robson letter sensitivity test, scored as number
of letters read correctly. These data are reported as log contrast sensitivity
(with the number of letters read of 48 letters indicated). Our previous article
demonstrated that models using vision in the better seeing eye were as good
as models using measures of binocular visual acuity in predicting self-reported
function.1 Other analyses also indicated negligible
contribution from the worse seeing eye in models of contrast sensitivity loss
and function. Therefore, for these analyses, we used presenting acuity and
contrast sensitivity in the better seeing eye.
OUTCOME MEASURES: PERFORMANCE ON TASKS
Details on the tasks and scoring procedures are described elsewhere.12-13 For the purposes of this study, we
chose performance-based tests in 3 categories: mobility, tasks of daily living
that require a visual component, and visually intensive tasks. Four measures
of mobility were included: a timed 4-m walk, a timed stair ascent, stair descent,
and a timed get-up-and-go test (the latter requires the participant to get
up from a chair with arms, and step away from the chair). The tasks of daily
living included 3 timed measures: inserting a key in a lock, inserting a plug
in a socket, and dialing a rotary telephone. The times to complete the task
for mobility items and items of daily living were converted to speed. All
tasks were performed in the clinic under standard conditions, including lighting
between 400 and 600 lux. The visually intensive tasks included face recognition
and reading speed. Face recognition involved presenting participants with
a panel of 4 faces (3 of the same individual) in different poses, and having
them select the one that differed from the other 3.1
The faces subtended 2.5° (horizontal) by 3.2° (vertical). Fifteen
sets of panels were presented on a monochrome monitor. Data were recorded
as number of faces identified correctly. Reading speed involved reading aloud
short passages of text presented on a computer screen for 15 seconds. Four
print sizes were tested; data for newsprint-sized text of 0.26° were used
in these analyses. Reading speed was calculated as the number of words read
correctly per minute.
DATA ANALYSES
The association between contrast sensitivity and visual acuity was examined
using a plot of logMAR vs contrast for right eyes, with correlation coefficient
and R2 calculated to determine the degree
of independence of the 2 measures. The distributions of the speeds for the
functional tests, and the score for face recognition, were examined for normalcy.
All speeds of task performance were converted to z
scores by subtracting the mean and dividing by the SE to permit meaningful
comparisons. Simple smoothed graphs, followed by linear spline regression
analyses that adjusted for age, sex, race, education, cognition, and number
of comorbid conditions, were done. Linear regression analyses were carried
out to determine the joint and unique contribution of visual acuity and contrast
sensitivity loss, adjusted for other predictors, to disability. The unit of
change was 0.1 logMAR for visual acuity, equivalent to 1 line change, and
0.05 log contrast sensitivity, equivalent to 1 letter for contrast sensitivity.
To characterize disability, we used a cutoff of 1 SD below the population
mean for performance on a test. Receiver operating characteristic (ROC) curves
were used to evaluate the sensitivity and specificity of acuity and/or contrast
sensitivity loss for predicting disability at this level of dichotomization.
Graphs were also created of the proportion disabled by level of visual acuity
and contrast sensitivity decrement.
RESULTS
The population in the SEE Project study was 58% female, and 26% were
African American (Table 1). The
distributions of each of the performance-based outcome measures were close
to normal, with the exception of face recognition where the distribution was
skewed to high performance. The mean and SD for each of the outcome measures
are given in Table 2.
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Table 1. Characteristics of 2520 Participants in the SEE Population*
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Table 2. Mean and SD of Performance-Based Outcome Measures of Function
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As expected, those with very good contrast sensitivity generally also
had good visual acuity as well. However, there was a substantial spread of
visual acuity scores in those with contrast sensitivity below 1.65 log units
(36 letters) (Figure 1). Overall,
the correlation was 0.81 with an R2 of
0.66. (Data shown for right eye.)
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Figure 1. Correlation between logMAR visual
acuity and log contrast sensitivity in the right eyes of the participants.
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For all the functional outcomes, the relationship with either visual
acuity or contrast sensitivity was close to linear, or linear with changes
in the slope. There was no evidence for a threshold of either acuity or contrast
sensitivity below which a sharp decrement in performance was observed; rather,
gradual declines in performance were observed concomitant with modest decrements
in visual acuity or contrast sensitivity. An example of a linear relationship
with a change in slope (where a spline regression was appropriate) is shown
in Figure 2A, where the slope of
the relationship with speed of plug insertion changes with loss of contrast
sensitivity. A straight linear relationship is typified by Figure 2B, the relationship between logMAR visual acuity and number
of faces read correctly. We created linear regression models, including spline
terms where significant, for visual acuity and contrast sensitivity separately
for each of the outcome measures (Table
3). For all the outcome measures (transformed into z scores so change is per SD unit), significant associations were observed
with age, race, sex, cognitive score, and number of comorbid conditions, as
well as the vision variables, and these confounders were also included.
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Figure 2. A, Smoothed graph of the relationship
between contrast sensitivity in the better eye and the score for speed of
inserting a plug. B, Smoothed graph of the relationship between visual acuity
in the better eye and the number of faces read correctly on the face recognition
test.
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Table 3. Regression Models of Associations Between Visual Acuity and
Contrast Sensitivity Loss and Performance-Based Tests of Function*
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To investigate the independent contributions of visual acuity loss and
contrast loss, both variables were included in final models for each outcome
in the set of mobility items, activities of daily living items, and visually
intensive tasks (Table 4). All
outcomes were significantly related to both measures of vision, except visual
acuity was unrelated to decreased speed of stair ascent or descend once contrast
sensitivity was included in the model. These data suggest that for daily living
tasks and reading speed, the sharpest declines were associated with a contrast
sensitivity between 0.85 and 1.60 log units (20-35 of 48 letters). For daily
living tasks, decrements were observed until visual acuity was about 0.7 logMAR
(20/100); for reading speed, the sharpest declines were observed until visual
acuity reached about 0.5 logMAR (20/60), then leveled off.
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Table 4. Joint Contribution of Visual Acuity Loss and Contrast Sensitivity
Loss to Performance*
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Except possibly for reading speed, there are no commonly accepted performance
standards for "disability" that could be applied to our data. Therefore, we
arbitrarily defined disability for each outcome as the value at 1 SD below
the mean value (the values are shown in Table 1). The essentially linear relationship between visual loss
and functional loss, and the effect of other comorbid conditions besides visual
loss on these functional decrements, suggested that there is no optimal cutoff
for either visual acuity or contrast that was both highly sensitive and highly
specific for disability in these domains. The ROC curves for each outcome
confirm this (Figure 3). Except
for reading, where for example at logMAR visual acuity value 0.176 there was
50% sensitivity and over 95% specificity, the other outcomes have true- and
false-positive rates that are almost equal. Thus, for any arbitrarily defined
cutoff for disabling visual acuity loss, there would be persons with less
visual loss with only slightly less risk of disability. Similar ROC curves
were observed for contrast sensitivity (data not shown).
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Figure 3. The level of visual acuity and
the percentage of subjects labeled "disabled" on the following everyday tasks:
walking speed (A), chair stand (B), stair ascent (C), stair descent (D), plug
insertion (E), key insertion (F), dialing a telephone (G), reading speed (H),
and face recognition (I).
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In light of these findings, we took another approach to evaluating the
effect of visual acuity and contrast sensitivity loss on disability: the determination
of the level of visual acuity loss or loss of contrast sensitivity at which
more than half of the affected population would be expected to be disabled
(ie, performing at levels <1 SD below that of the population mean). For
each of the domains, that level was different, as might be expected by the
visual demands of the tasks (Table 5).
Mobility was unaffected by visual acuity loss until levels worse than 1.0
logMAR (20/200) and even then, a sizable number were not disabled. However,
for daily living tasks and visually intensive tasks, the cutoff point for
50% disabled occurred at more modest levels of visual acuity and contrast
sensitivity loss. Disability in reading, that is, reading fewer than 90 words
per minute, was observed in the 50% of the population with visual acuity worse
than 0.2 logMAR (20/30) and in 90% of those with acuity worse than 0.3 logMAR
(20/40). Disability in reading speed was also sensitive to contrast sensitivity
loss, with 50% disabled among those with contrast sensitivity of 1.4 log units
or worse (seeing  31 letters), and 90% disabled among those with contrast
sensitivity of 0.55 log units (14 letters) or fewer.
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Table 5. Level of Visual Acuity and Contrast Sensitivity in the Better
Eye Associated With at Least 50% of the Population With That Vision Performing
in the Disabled Range*
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COMMENT
The loss of vision has been associated with numerous deleterious outcomes,
from decrements in self-report of function,3-7,19-20
use of social services,21 nursing home admissions,22 and even mortality.23-25
However, few population-based studies have attempted to determine the effect
of vision on actual performance in tasks of everyday life. Performance-based
tests of function provide data that is different from self-reported function
in several important ways.26 First, standardized
testing provides a continuous measure of performance across the population
that is not subject to vagaries of self-report. Self-report of function combines
an individual's assessment of his or her ability, his or her expectation of
that ability, and his or her understanding of the degree of difficulty involved
in carrying out the task in the presence of any limitations. Performance-based
testing requires the individual to simply do the task. Second, it has been
argued that cognitively impaired persons can perform a task, whereas self-report
of difficulty performing the task may be inaccurate. In our study, severe
cognitive impairment was not an issue. Finally, performance-based tests usually
provide a continuous measure of function that may be more sensitive to change
over time compared with categorical data. Performance-based tests as carried
out in research settings are criticized as unrepresentative as they are conducted
under ideal conditions (lighting among others). However, we have found that
performance at the clinic and the performance at home on the same tasks were
highly correlated.13 Moreover, normative data
in representative populations enable the determination of performance levels
that are distinctly poorer than the average performance. If curves indicating
a sharp dropoff in performance were observed, it would enable ROC curve analyses
to determine possible cutoff point values for visual loss that had good sensitivity
and specificity for poor performance.
In our population-based study of 2520 persons aged from 65 to 84 years,
we found reasonably normal distributions of performance on tasks in domains
of mobility, activities of daily life, and visually intensive tasks (with
the exception of the face recognition test, which was skewed to high performance).
Moreover, both visual acuity and contrast sensitivity were significantly related
to performance on all the tasks, except stair ascent and descent where contrast
sensitivity alone predicted performance. Our data suggest that contrast sensitivity
loss and visual acuity loss each contribute independently to decrements in
performance. Together with other cofactors, the vision variables predicted
anywhere from 21% to 57% of the variance associated with the outcome of interest.
Linear relationships between the functional tasks and visual acuity
and contrast, with some spline regressions where the slope changed, were the
best characterization of the association. Thus, decrements in visual acuity
from 0.0 logMAR (20/20) or better, or in contrast from about 1.85 log units
(40 letters), were likely to have a negative effect on performance, and any
cutoff point to determine visual disability would be arbitrary. This finding
was further supported by the use of ROC curve analyses.
To explore the issue of vision loss associated with functional disability
using ROC curve analyses required us to define disability for these tests.
We chose to define disability as performance less than 1 SD below the population
mean. Our selection allowed us to have reasonable numbers in the disabled
group, and yet have disability cutoff points that were marked. For example,
the cutoff point of less than 1 SD defined as disabled those who read standard
newsprint-size letters at fewer than 90 words per minute, or recognized about
half the faces in the test, or required close to 19 seconds to insert a key
into a lock. Regardless of the definition of disability, though, no level
of vision (either visual acuity or contrast sensitivity) provided reasonable
values for sensitivity and specificity in determining disability. This result
was not surprising, given the nearly linear relationship of the vision variables
with performance on the tasks, and the contribution of other factors to performance.
An alternative approach is proposed: determining at what level of visual
acuity or contrast sensitivity loss more than 50% of the population is disabled
(in our study, disabled was defined as performance on tasks 1 SD below the
population mean, but it could be defined in other ways). Using this approach,
the variation in level of visual acuity or contrast sensitivity loss is easily
observed, according to the degree of visual demand required to do the task.
For mobility tasks, less than 50% of persons are disabled until visual acuity
becomes worse than 1.0 logMAR (20/200). We were unable to determine at what
point beyond 1.0 logMAR the cutoff point would be reached because of small
numbers in our population with vision worse than 1.0 logMAR.
However, for reading speed or face recognition, visual acuity loss as
modest as 0.3 logMAR (20/40) and contrast sensitivity loss of 1.35 log units
(30 letters) was associated with disability, values of impairment that are
otherwise considered rather minor. Leat et al27
suggest that a log contrast sensitivity score of less than 1.5 log units (33
letters) is impaired, while a score of 1.05 would result in disability. Our
data suggest that the level of disability associated with vision loss is highly
dependent on the task and the visual demands of the task. Thus, the use of
an arbitrary cutoff point of visual impairment to define disability may well
ignore disabilities experienced at lesser levels of visual acuity or contrast
loss.
There are limitations to our study that must be discussed. First, this
is a cross-sectional analysis of associations. Because we do not know the
time of visual loss or the onset of difficulty in performing tasks, these
associations should not be interpreted as causal. Moreover, it is likely that
those with visual loss of longer duration have arrived at compensatory strategies
for performing tasks that persons with more recent onset have not yet learned.
It will be critical to add a longitudinal component to this study.
In addition, the level of visual acuity or contrast sensitivity loss
associated with disability will be sensitive to the definition of disability.
The question of whether a significant slowing of speed in performing a task
is a true problem in everyday life of our participants was not addressed.
For a few, a reading speed of fewer than 90 words per minute was reported
as not a significant problem, while others reported extreme difficulty or
inability to read at such a speed.28 For this
analysis, we chose 1 SD below the mean, and that choice resulted in performances
that were clearly abnormal. However, defining disability as 2 SDs below the
mean would result in great loss of visual acuity and contrast sensitivity
necessary for disability. Regardless of the cutoff point for disability, though,
the same linear function will be operative.
Finally, many factors, apart from vision, have an effect on the ability
of persons to perform everyday activities. In the SEE Project, we found that
age, sex, race, education, and comorbid conditions also affected performance
on tasks in several domains. Our study was carried out in an aging population,
so it is not surprising that these other factors also are involved in the
performance on tasks. We have tried to control for these factors by models
that adjust for confounding. However, it is clear that using a visual acuity
or contrast sensitivity cutoff point to predict performance on tests of function,
even highly visually demanding tests, will never be able to simultaneously
achieve reasonable sensitivity and specificity.
CONCLUSIONS
This study has documented the independent contributions of visual acuity
and contrast sensitivity loss to decrease in performance in a variety of tasks
of daily living. The relationships between visual loss and performance are
essentially linear, with no obvious thresholds for sharp declines in performance.
Using a population-based approach to defining disability, it is apparent that
varying levels of visual acuity or contrast sensitivity loss that are associated
with most of the population at that level of loss being disabled can vary,
depending on the task. Choosing any cutoff point for disability is arbitrary.
A more scientific approach to disability would consider the level of function
for a given task that is required, and the likelihood that persons with visual
acuity or contrast sensitivity impairments would be able to perform at that
level.
AUTHOR INFORMATION
Submitted for publication February 28, 2001; final revision received
October 23, 2001; accepted November 17, 2001.
This study was funded by award PO1-AG10184 from the National Institute
on Aging, Bethesda, Md.
Dr West is a Research to Prevent Blindness Senior Scientific Investigator.
Dr Bandeen-Roche is a Brookdale National Fellow.
The members of the SEE Project Team are as follows: Christine Alston;
David Alston; Millie Hernandez, BA; Jennifer Istre-Walker, MPH; Michelle Johnson;
Shelley Jones, BS; Carolyn Porter Long, MA; and Jacquelyn Townsend.
Corresponding author: Sheila K. West, PhD, Wilmer Eye Institute,
Room 129 K. Johns Hopkins University, 600 N Wolfe St, Baltimore, MD 21287
(e-mail: shwest{at}jhmi.edu).
Sheila K. West, PhD;
Gary S. Rubin, PhD;
Aimee T. Broman, MS;
Beatriz Muñoz, MSc;
Karen Bandeen-Roche, PhD;
Kathleen Turano, PhD;
for the SEE Project Team
From the Dana Center for Preventive Ophthalmology, Wilmer Eye Institute,
The Johns Hopkins University School of Medicine, Baltimore, Md (Dr West and
Mss Broman and Muñoz); Institute of Ophthalmology, University College,
London, England (Dr Rubin); Department of Biostatistics, Johns Hopkins Bloomberg
School of Public Health, Baltimore (Dr Bandeen-Roche); and Lions Low Vision
Center, Wilmer Eye Institute, The Johns Hopkins University School of Medicine,
Baltimore (Dr Turano).
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